In this paper, we have presented an efficient method for sparse identification of dynamical systems described by ordinary differential equations. We have shown that the Levenberg-Marquardt algorithm, on which our method is based, can be rewritten in a form that enables parallel computation to a large extent. Therefore, the wall time required to solve the identification problem can be greatly decreased. In addition, we have presented a sparsification approach based on backward elimination that utilizes the same time-efficient computation strategy to estimate the relevance of any given network edge, so that only those network edges that have a small effect on the quality of the model fit are removed. We have illustrated with two examples that our method is not affected by many of the limitations, such as fixed sampling rates, full state measurements, and linearity of the model, that plague other methods (e.g., in [1]} and [2]}). This flexibility is essential for the applicability of the method in many industrial settings. The main drawback of our approach is the relatively large computational demand. However, utilizing the time-efficient algorithm presented in this paper, the wide applicability of our method is likely to outweigh the larger computational cost in many practical scenarios.
